Donor Nerve Selection in Free Gracilis Muscle Transfer; Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: One of the crucial factors in facial reanimation surgery is the donor nerve selection. There have been several neurotizers to reinnervate the free gracilis muscle flap (FGMT). Currently the cross-face nerve graft (CFNG) and motor nerve to masseter are the most favored options. The “dual innervation” is described as potential way to combine the advantages of individual donor nerves and has shown successful results. Materials and Methods: Two databases were searched for the CFNG, masseteric nerve and dual innervation of FGMT. Defined primary measures were applied to the results to compare the extent of commissure excursion, facial symmetry at rest and smile, spontaneity, time to flap contraction for each neurotizer. Meta-analysis was conducted for studies including quantitative data for commissure excursion and facial symmetry. We included all study types except the animal studies. Only articles written in English language from 2001 were taken into consideration. We followed PRISMA guidelines. ROBINS-I and Newcastle-Ottawa tools were used to assess the risk of bias and quality for each study included in the meta-analysis. Results: 147 articles on FGMT were systematically reviewed and 13 studies were evaluated in the meta-analysis. All studies were high in quality. Heterogeneity was high among the studies, limiting the efficiency of our meta-analysis. Conclusion: CFNG is the most frequently utilized reinnervation technique. Masseteric nerve is very reliable and has the potential to provide more commissural excursion and higher success rate. Dual innervation is an effective alternative leading to effective clinical outcomes. A globalized and quantitative measurement method will contribute to the future of facial reanimation surgery.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".